{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import logging\n",
    "import pickle\n",
    "from pathlib import Path\n",
    "from time import perf_counter\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "from mcmc import MCMC\n",
    "from mcmc.system import SurfaceSystem\n",
    "from mcmc.utils import setup_logger\n",
    "from mcmc.utils.plot import plot_summary_stats\n",
    "\n",
    "np.set_printoptions(precision=3, suppress=True)\n",
    "# uncomment and replace with correct path if there are errors with LAMMPS\n",
    "# os.environ[\"LAMMPS_POTENTIALS\"] = \"/path/to/lammps/potentials/\"\n",
    "# os.environ[\"LAMMPS_COMMAND\"] =\"/path/to/lammps/src/lmp_serial\"\n",
    "# os.environ[\"ASE_LAMMPSRUN_COMMAND\"] = os.environ[\"LAMMPS_COMMAND\"]"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize test slab and parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Initialize paths\n",
    "surface_name = \"GaN_0001\"\n",
    "run_folder = Path() / surface_name\n",
    "run_folder.mkdir(parents=True, exist_ok=True)\n",
    "\n",
    "# Initialize logger\n",
    "logger = setup_logger(\"mcmc\", run_folder / \"mc.log\", logging.INFO)\n",
    "logging.getLogger(\n",
    "    \"matplotlib.font_manager\"\n",
    ").disabled = True  # disable matplotlib font manager logging\n",
    "\n",
    "# Load prepared pristine slab\n",
    "try:\n",
    "    with open(\"data/GaN_0001/GaN_0001_3x3_pristine_slab.pkl\", \"rb\") as f:\n",
    "        slab = pickle.load(f)\n",
    "except FileNotFoundError as e:\n",
    "    print(\"Pristine surface pkl file not found. Please check you have downloaded the data.\")\n",
    "    raise e\n",
    "\n",
    "calc_settings = {\n",
    "    \"calc_name\": \"LAMMPS\",\n",
    "    \"optimizer\": \"LAMMPS\",\n",
    "    \"chem_pots\": {\"Ga\": 5},  # eV. arbitrary value\n",
    "    \"relax_atoms\": True,\n",
    "    \"relax_steps\": 100,\n",
    "    \"run_dir\": run_folder,\n",
    "}\n",
    "\n",
    "system_settings = {\n",
    "    \"surface_name\": surface_name,\n",
    "    \"surface_depth\": 1,\n",
    "    \"cutoff\": 5.0,\n",
    "    \"near_reduce\": 0.01,\n",
    "    \"planar_distance\": 1.5,\n",
    "    \"no_obtuse_hollow\": True,\n",
    "    \"ads_site_type\": \"all\",\n",
    "}\n",
    "\n",
    "sampling_settings = {\n",
    "    \"total_sweeps\": 10,\n",
    "    \"sweep_size\": 104,\n",
    "    \"start_temp\": 1.0,  # in terms of kbT\n",
    "    \"perform_annealing\": True,\n",
    "    \"alpha\": 0.99,  # slowly anneal\n",
    "    \"canonical\": True,\n",
    "    \"num_ads_atoms\": 12,\n",
    "    \"adsorbates\": [\"Ga\"],\n",
    "    \"run_folder\": run_folder,\n",
    "}"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Set up LAMMPS (calculator). Requires a `lammps_config.json` and a `lammps_template.txt` in `run_dir`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:07:10 - mcmc.calculators | INFO: run directory: GaN_0001 is set from parameters\n",
      "10:07:10 - mcmc.calculators | INFO: relaxation steps: 100 is set from parameters\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/dux/NeuralForceField/models\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'calc_name': 'LAMMPS',\n",
       " 'optimizer': 'LAMMPS',\n",
       " 'chem_pots': {'Ga': 5},\n",
       " 'relax_atoms': True,\n",
       " 'relax_steps': 100,\n",
       " 'run_dir': PosixPath('GaN_0001')}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from mcmc.calculators import LAMMPSSurfCalc\n",
    "\n",
    "lammps_surf_calc = LAMMPSSurfCalc()\n",
    "lammps_surf_calc.set(**calc_settings)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize surface system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:07:10 - mcmc.system | INFO: Initalizing adsorption sites with settings: {'surface_name': 'GaN_0001', 'surface_depth': 1, 'cutoff': 5.0, 'near_reduce': 0.01, 'planar_distance': 1.5, 'no_obtuse_hollow': True, 'ads_site_type': 'all'}\n",
      "10:07:11 - mcmc.system | INFO: Generated adsorption coordinates are: [array([ 0.   ,  0.   , 14.766]), array([12.865,  7.428, 14.12 ]), array([12.865,  5.571, 14.766]), array([11.257,  4.642, 14.12 ]), array([ 1.608,  2.785, 14.766])]...\n",
      "10:07:11 - mcmc.system | INFO: Initializing 107 virtual atoms\n",
      "10:07:11 - mcmc.system | INFO: Initial state is [0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0]\n",
      "10:07:11 - mcmc.system | INFO: Number of pristine atoms is 36\n",
      "10:07:11 - mcmc.system | INFO: Bulk indices are [ 1  2  3  5  6  7  9 10 11 13 14 15 17 18 19 21 22 23 25 26 27 29 30 31\n",
      " 33 34 35]\n",
      "10:07:11 - mcmc.system | INFO: Surface indices are [ 0  4  8 12 16 20 24 28 32]\n",
      "10:07:11 - mcmc.system | INFO: Constraints are FixAtoms(indices=[1, 2, 3, 5, 6, 7, 9, 10, 11, 13, 14, 15, 17, 18, 19, 21, 22, 23, 25, 26, 27, 29, 30, 31, 33, 34, 35])\n",
      "10:07:11 - mcmc.system | INFO: Relaxing initialized surface\n",
      "10:07:11 - mcmc.calculators | INFO: None\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total wall time: 0:00:00\n"
     ]
    }
   ],
   "source": [
    "surface = SurfaceSystem(\n",
    "    slab,\n",
    "    calc=lammps_surf_calc,\n",
    "    system_settings=system_settings,\n",
    "    save_folder=run_folder,\n",
    ")\n",
    "surface.all_atoms.write(run_folder / \"GaN_0001_3x3_all_virtual_ads.cif\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test calculate pristine surface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:07:11 - mcmc.calculators | INFO: None\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total wall time: 0:00:00\n",
      "Energy -144.059 eV\n"
     ]
    }
   ],
   "source": [
    "print(f\"Energy {surface.get_potential_energy():.3f} eV\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Perform MCMC and view results. Detailed results can be found in the corresponding run in the `GaN(0001)/` folder."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:07:11 - mcmc.mcmc | INFO: Using run folder GaN_0001\n",
      "10:07:11 - mcmc.mcmc | INFO: Randomly adsorbing sites\n",
      "10:07:11 - mcmc.events.criterion | ERROR: No surface energy found in results dict\n",
      "10:07:11 - mcmc.calculators | INFO: None\n",
      "10:07:11 - mcmc.calculators | INFO: None\n",
      "10:07:11 - mcmc.calculators | INFO: None\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:07:11 - mcmc.calculators | INFO: None\n",
      "10:07:11 - mcmc.calculators | INFO: None\n",
      "10:07:11 - mcmc.calculators | INFO: None\n",
      "10:07:11 - mcmc.calculators | INFO: None\n",
      "10:07:11 - mcmc.calculators | INFO: None\n",
      "10:07:11 - mcmc.calculators | INFO: None\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total wall time: 0:00:00\n",
      "Total wall time: 0:00:00\n",
      "Total wall time: 0:00:00\n",
      "Total wall time: 0:00:00\n",
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      "Total wall time: 0:00:00\n",
      "Total wall time: 0:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:07:11 - mcmc.calculators | INFO: None\n",
      "10:07:11 - mcmc.calculators | INFO: None\n",
      "10:07:11 - mcmc.calculators | INFO: None\n",
      "10:07:11 - mcmc.calculators | INFO: None\n",
      "10:07:11 - mcmc.calculators | INFO: None\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total wall time: 0:00:00\n",
      "Total wall time: 0:00:00\n",
      "Total wall time: 0:00:00\n",
      "Total wall time: 0:00:00\n",
      "Total wall time: 0:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:07:11 - mcmc.calculators | INFO: None\n",
      "10:07:11 - mcmc.calculators | INFO: None\n",
      "10:07:11 - mcmc.calculators | INFO: None\n",
      "10:07:11 - mcmc.calculators | INFO: None\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total wall time: 0:00:00\n",
      "Total wall time: 0:00:00\n",
      "Total wall time: 0:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:07:11 - mcmc.calculators | INFO: None\n",
      "10:07:11 - mcmc.calculators | INFO: None\n",
      "10:07:11 - mcmc.calculators | INFO: None\n",
      "10:07:11 - mcmc.calculators | INFO: None\n",
      "10:07:11 - mcmc.calculators | INFO: None\n",
      "10:07:11 - mcmc.calculators | INFO: None\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total wall time: 0:00:00\n",
      "Total wall time: 0:00:00\n",
      "Total wall time: 0:00:00\n",
      "Total wall time: 0:00:00\n",
      "Total wall time: 0:00:00\n",
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      "Total wall time: 0:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:07:11 - mcmc.calculators | INFO: None\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total wall time: 0:00:00\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:07:12 - mcmc.mcmc | INFO: There are 36 atoms in pristine slab\n",
      "10:07:12 - mcmc.mcmc | INFO: Running with total_sweeps = 10, sweep_size = 104, start_temp = 1.000\n",
      "10:07:12 - mcmc.mcmc | INFO: Starting with iteration 0\n",
      "10:07:12 - mcmc.mcmc | INFO: Temperature schedule is: ['1.000', '0.990', '0.980', '0.970', '0.961', '0.951', '0.941', '0.932', '0.923', '0.914']\n",
      "10:07:12 - mcmc.mcmc | INFO: In sweep 1 out of 10\n",
      "10:07:12 - mcmc.calculators | INFO: None\n",
      "10:07:12 - mcmc.calculators | INFO: None\n",
      "10:07:12 - mcmc.calculators | INFO: None\n",
      "10:07:12 - mcmc.calculators | INFO: None\n",
      "10:07:12 - mcmc.calculators | INFO: None\n",
      "10:07:12 - mcmc.calculators | INFO: None\n",
      "10:07:12 - mcmc.calculators | INFO: None\n",
      "10:07:12 - mcmc.calculators | INFO: None\n",
      "10:07:12 - mcmc.calculators | INFO: None\n",
      "10:07:12 - mcmc.calculators | INFO: None\n",
      "10:07:12 - mcmc.calculators | INFO: None\n",
      "10:07:12 - mcmc.calculators | INFO: None\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total wall time: 0:00:00\n",
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    },
    {
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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    },
    {
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    },
    {
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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    {
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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      "Total wall time: 0:00:00\n",
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    },
    {
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     "output_type": "stream",
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    },
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     "output_type": "stream",
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      "10:07:16 - mcmc.mcmc | INFO: At iter 104\n"
     ]
    },
    {
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     "output_type": "stream",
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      "10:07:16 - mcmc.system | INFO: Optimized structure has surface energy = -174.577\n",
      "10:07:16 - mcmc.mcmc | INFO: In sweep 2 out of 10\n",
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    },
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    },
    {
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:07:21 - mcmc.calculators | INFO: None\n",
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      "10:07:21 - mcmc.calculators | INFO: None\n",
      "10:07:21 - mcmc.calculators | INFO: None\n",
      "10:07:21 - mcmc.mcmc | INFO: At iter 208\n",
      "10:07:21 - mcmc.calculators | INFO: None\n",
      "10:07:21 - mcmc.calculators | INFO: None\n",
      "10:07:21 - mcmc.calculators | INFO: None\n",
      "10:07:21 - mcmc.system | INFO: Optimized structure has surface energy = -177.414\n",
      "10:07:21 - mcmc.mcmc | INFO: In sweep 3 out of 10\n",
      "10:07:21 - mcmc.calculators | INFO: None\n",
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     ]
    },
    {
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     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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     ]
    },
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     ]
    },
    {
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     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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    },
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     ]
    },
    {
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     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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     ]
    },
    {
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      "Total wall time: 0:00:00\n",
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     ]
    },
    {
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     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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    {
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     ]
    },
    {
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    },
    {
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     ]
    },
    {
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     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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    {
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    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
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    },
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    },
    {
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     "output_type": "stream",
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    },
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    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
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      "Total wall time: 0:00:00\n",
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      "Total wall time: 0:00:00\n",
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    {
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    },
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      "Total wall time: 0:00:00\n",
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    },
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Total wall time: 0:00:00\n",
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     ]
    },
    {
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     "output_type": "stream",
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      "10:07:26 - mcmc.calculators | INFO: None\n",
      "10:07:26 - mcmc.calculators | INFO: None\n",
      "10:07:26 - mcmc.mcmc | INFO: At iter 312\n",
      "10:07:26 - mcmc.calculators | INFO: None\n",
      "10:07:26 - mcmc.calculators | INFO: None\n",
      "10:07:26 - mcmc.system | INFO: Optimized structure has surface energy = -176.994\n",
      "10:07:26 - mcmc.mcmc | INFO: In sweep 4 out of 10\n",
      "10:07:26 - mcmc.calculators | INFO: None\n",
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     ]
    },
    {
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     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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    },
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     ]
    },
    {
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     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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    },
    {
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     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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    },
    {
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     "output_type": "stream",
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    {
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    },
    {
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     "output_type": "stream",
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    {
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    },
    {
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     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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    },
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
     "text": [
      "Total wall time: 0:00:00\n",
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    },
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Total wall time: 0:00:00\n",
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    },
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Total wall time: 0:00:00\n",
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     ]
    },
    {
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    },
    {
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      "Total wall time: 0:00:00\n",
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      "Total wall time: 0:00:00\n",
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    },
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     ]
    },
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      "Total wall time: 0:00:00\n",
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     ]
    },
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     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
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     ]
    },
    {
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     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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    },
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Total wall time: 0:00:00\n",
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     ]
    },
    {
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     "output_type": "stream",
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      "10:07:30 - mcmc.calculators | INFO: None\n",
      "10:07:30 - mcmc.mcmc | INFO: At iter 416\n",
      "10:07:30 - mcmc.calculators | INFO: None\n",
      "10:07:30 - mcmc.calculators | INFO: None\n",
      "10:07:30 - mcmc.calculators | INFO: None\n",
      "10:07:30 - mcmc.system | INFO: Optimized structure has surface energy = -178.785\n",
      "10:07:30 - mcmc.mcmc | INFO: In sweep 5 out of 10\n",
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     ]
    },
    {
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     "output_type": "stream",
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    },
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    },
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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    },
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     "output_type": "stream",
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    },
    {
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    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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     ]
    },
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      "Total wall time: 0:00:00\n",
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     "output_type": "stream",
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      "10:07:35 - mcmc.calculators | INFO: None\n",
      "10:07:35 - mcmc.mcmc | INFO: At iter 520\n",
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      "10:07:35 - mcmc.calculators | INFO: None\n",
      "10:07:35 - mcmc.system | INFO: Optimized structure has surface energy = -177.173\n",
      "10:07:35 - mcmc.mcmc | INFO: In sweep 6 out of 10\n",
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    },
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    },
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    },
    {
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    },
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Total wall time: 0:00:00\n",
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    },
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     "output_type": "stream",
     "text": [
      "10:07:40 - mcmc.calculators | INFO: None\n",
      "10:07:40 - mcmc.calculators | INFO: None\n",
      "10:07:40 - mcmc.mcmc | INFO: At iter 624\n",
      "10:07:40 - mcmc.calculators | INFO: None\n",
      "10:07:40 - mcmc.calculators | INFO: None\n",
      "10:07:40 - mcmc.calculators | INFO: None\n",
      "10:07:40 - mcmc.system | INFO: Optimized structure has surface energy = -178.399\n",
      "10:07:40 - mcmc.mcmc | INFO: In sweep 7 out of 10\n",
      "10:07:40 - mcmc.calculators | INFO: None\n",
      "10:07:40 - mcmc.calculators | INFO: None\n",
      "10:07:40 - mcmc.calculators | INFO: None\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total wall time: 0:00:00\n",
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     ]
    },
    {
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     ]
    },
    {
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     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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     ]
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     ]
    },
    {
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     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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    {
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     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total wall time: 0:00:00\n",
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     ]
    },
    {
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Total wall time: 0:00:00\n",
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     ]
    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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    },
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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    },
    {
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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    },
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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    },
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      "10:07:43 - mcmc.calculators | INFO: None\n",
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     ]
    },
    {
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      "Total wall time: 0:00:00\n",
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     ]
    },
    {
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      "Total wall time: 0:00:00\n",
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     ]
    },
    {
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     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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     ]
    },
    {
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     "output_type": "stream",
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      "10:07:44 - mcmc.calculators | INFO: None\n",
      "10:07:44 - mcmc.mcmc | INFO: At iter 728\n"
     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Total wall time: 0:00:00\n",
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     ]
    },
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     "output_type": "stream",
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      "10:07:44 - mcmc.system | INFO: Optimized structure has surface energy = -176.466\n",
      "10:07:44 - mcmc.mcmc | INFO: In sweep 8 out of 10\n",
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     ]
    },
    {
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     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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    },
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     ]
    },
    {
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     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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     ]
    },
    {
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     ]
    },
    {
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     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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    },
    {
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     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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    {
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    },
    {
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     "output_type": "stream",
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     "output_type": "stream",
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    },
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    },
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      "10:07:47 - mcmc.calculators | INFO: None\n",
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      "Total wall time: 0:00:00\n",
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      "Total wall time: 0:00:00\n",
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     ]
    },
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    },
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Total wall time: 0:00:00\n",
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     ]
    },
    {
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     "output_type": "stream",
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      "10:07:49 - mcmc.calculators | INFO: None\n",
      "10:07:49 - mcmc.mcmc | INFO: At iter 832\n",
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      "10:07:49 - mcmc.calculators | INFO: None\n",
      "10:07:49 - mcmc.calculators | INFO: None\n",
      "10:07:49 - mcmc.system | INFO: Optimized structure has surface energy = -177.305\n",
      "10:07:49 - mcmc.mcmc | INFO: In sweep 9 out of 10\n"
     ]
    },
    {
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     ]
    },
    {
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      "Total wall time: 0:00:00\n",
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     ]
    },
    {
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     "output_type": "stream",
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      "Total wall time: 0:00:00\n",
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    },
    {
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     "output_type": "stream",
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      "10:07:53 - mcmc.calculators | INFO: None\n",
      "10:07:53 - mcmc.mcmc | INFO: At iter 936\n",
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      "10:07:53 - mcmc.calculators | INFO: None\n",
      "10:07:53 - mcmc.system | INFO: Optimized structure has surface energy = -178.319\n",
      "10:07:53 - mcmc.mcmc | INFO: In sweep 10 out of 10\n",
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     ]
    },
    {
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     "output_type": "stream",
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    },
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     "output_type": "stream",
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     ]
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    },
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     "output_type": "stream",
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    },
    {
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     "output_type": "stream",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
      "Total wall time: 0:00:00\n",
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     ]
    },
    {
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     "output_type": "stream",
     "text": [
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     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total wall time: 0:00:00\n",
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     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "10:07:57 - mcmc.calculators | INFO: None\n",
      "10:07:57 - mcmc.calculators | INFO: None\n",
      "10:07:57 - mcmc.mcmc | INFO: At iter 1040\n",
      "10:07:57 - mcmc.calculators | INFO: None\n",
      "10:07:57 - mcmc.calculators | INFO: None\n",
      "10:07:57 - mcmc.system | INFO: Optimized structure has surface energy = -177.075\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total wall time: 0:00:00\n",
      "Total wall time: 0:00:00\n",
      "Total wall time: 0:00:00\n",
      "Total wall time: 0:00:00\n",
      "Time taken = 46.306 seconds\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mcmc = MCMC(**sampling_settings)\n",
    "\n",
    "start = perf_counter()\n",
    "results = mcmc.run(\n",
    "    surface,\n",
    "    **sampling_settings,\n",
    ")\n",
    "stop = perf_counter()\n",
    "print(f\"Time taken = {stop - start:.3f} seconds\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Plot results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 3000x800 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plot_summary_stats(\n",
    "    results[\"energy_hist\"],\n",
    "    results[\"frac_accept_hist\"],\n",
    "    results[\"adsorption_count_hist\"],\n",
    "    sampling_settings[\"total_sweeps\"],\n",
    "    save_folder=run_folder,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.8.13 ('surface_sampling': conda)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.9"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "e3e0723b7fd9866ee8f9ae4f62931968cf8456ef2195b337b8930ae9f61708cf"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
